Abstract
This paper proposes an improved GA (genetic algorithm)-based integrated optimization of automated container terminal scheduling. The three-stage integrated optimization model of automated container terminal scheduling is suggested, and the objective is the minimal operation time of the loading and unloading tasks at the automated container terminal. To solve the difficult combination problem, an improved GA, which is named PGA (Probability Genetic Algorithm), is developed. As traditional GA does not change the probability according to the specific iterations in the population, PGA improves the above limitation to improve population distribution and accelerate convergence. Different from published literature, the study of this paper can be presented in two aspects. One is in the modeling; it includes (i) formalizing the description of the purpose of the model and (ii) having a real-world coordination of three types of equipment that are incorporated at automated container terminals. The other is that PGA is applied to deal with the integrated scheduling whose results can be gotten with better solving speed and convergence. Numerical experiments show that the model constructed in this paper has important reference value for the optimum ratio of QCs, AGVs, and ASCs in automated container operation, which is of great significance to improve the efficiency of the automated terminal. Furthermore, compared with the results of traditional GA and PSO (particle swarm optimization), the speed and convergence of PGA have been greatly improved.
Highlights
Introduction and Literature ReviewIn order to improve efficiency and profits, traditional container terminals began to use automated technology and stepped into the process of automation construction
Different choices of equipment make up different operation schemes of the automated container terminal scheduling. is research is based on the background of “Double-Trolley Quay Cranes (QCs) + Automatic Guide Vehicles (AGVs) + Automatic Stacking Cranes (ASCs)” loading and unloading system of Shanghai automated container terminal at Yang Shan Port. e threelevel integrated scheduling optimization model of the automated container terminal operation system is established, and an improved Genetic algorithms (GAs) is designed to solve the problem in this paper. e objective is minimizing the completion time of all the tasks, called the makespan, subject to the constraints. e optimization operation scheme formed in this paper can provide research support to optimize the automated terminal scheduling
Problem Description and Assumptions e automated production control system is used as the “Brain” to control the automated operations of the loading and unloading and transportation equipment at Shanghai automated container terminal of Yang Shan Port. e system consists of three types of equipment, namely, Quay Cranes (QCs), Automated Guided Vehicle (AGVs), and Automatic Stacking Cranes (ASCs)
Summary
In order to improve efficiency and profits, traditional container terminals began to use automated technology and stepped into the process of automation construction. E threelevel integrated scheduling optimization model of the automated container terminal operation system is established, and an improved GA is designed to solve the problem in this paper. Luo et al studied the AGV scheduling in the unloading process of automated container terminals, constructed the MIP model according to the integrated unloading problem, and designed an effective solution [22]. In order to realize the optimal scheduling of the whole system, Le et al proposed a new joint scheduling model of QCs, AGVs, and stacking cranes, which took the whole horizontal transportation system of the automated container terminal as the research object [26]. As there are conflicts among the optimization objectives of the QCs, AGVs, and ASCs, the threestage research in this paper is more comprehensive and practical. e other is that an improved GA, which is named PGA, is proposed in this paper. e crossover and variation probability in PGA change with the dispersion of the population, which can improve the convergence speed of the
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